Accurate inference of stochastic gene expression from nascent transcript heterogeneity

Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers measured using smFISH (single molecule fluorescence in situ hybridization) with the distribution predicted by the telegraph model of gene expression, which defines two promoter states of activity and inactivity. However, fluctuations in mature mRNA numbers are strongly affected by processes downstream of transcription. In addition, the telegraph model assumes one gene copy, but in experiments cells may have two gene copies as cells replicate their genome during the cell cycle. It is thus unclear how accurately the inferred parameters reflect transcription. To address these issues, here we measure both mature and nascent mRNA distributions of GAL10 in yeast cells using smFISH and classify each cell according to its cell cycle stage. We infer transcriptional parameters from mature and nascent mRNA distributions, with and without accounting for cell cycle stage and compare the results to live-cell transcription measurements of the same gene. We conclude that: (i) not accounting for cell cycle dynamics in nascent mRNA data overestimates the magnitude of promoter switching rates and the initiation rate, and underestimates the fraction of time spent in the active state and the burst size. (ii) use of mature mRNA data, instead of nascent data, significantly increases the errors in parameter estimation and can mistakenly classify a gene as non-bursting. Furthermore, we show how to correctly adjust for measurement noise in smFISH at low nascent transcript numbers. Simulations with parameters estimated from nascent smFISH data corrected for cell cycle phases and measurement noise leads to autocorrelation functions that agree with those obtained from live-cell imaging. Therefore, our novel data curation method yields a quantitatively accurate picture of gene expression.

The Exon Junction Complex Core Represses Cancer-Specific Mature mRNA Re-Splicing: A Potential Key Role in Terminating Splicing

Using TSG101 pre-mRNA, we previously discovered cancer-specific re-splicing of mature mRNA that generates aberrant transcripts/proteins. The fact that mRNA is aberrantly re-spliced in various cancer cells implies there must be an important mechanism to prevent deleterious re-splicing on the spliced mRNA in normal cells. We thus postulated that mRNA re-splicing is controlled by specific repressors, and we searched for repressor candidates by siRNA-based screening for mRNA re-splicing activity. We found that knock-down of EIF4A3, which is a core component of the exon junction complex (EJC), significantly promoted mRNA re-splicing. Remarkably, we could recapitulate cancer-specific mRNA re-splicing in normal cells by knock-down of any of the core EJC proteins, EIF4A3, MAGOH, or RBM8A (Y14), implicating the EJC core as the repressor of mRNA re-splicing often observed in cancer cells. We propose that the EJC core is a critical mRNA quality control factor to prevent over-splicing of mature mRNA.

The Exon Junction Complex Core Represses Caner-specific Mature mRNA Re-splicing: A Potential Key Role in Terminating Splicing

Using the TSG101 pre-mRNA, we previously discovered cancer-specific re-splicing of mature mRNA that generates aberrant transcripts/proteins. The fact that mRNA is aberrantly re-spliced in various cancer cells implies there must be an important mechanism to prevent deleterious re-splicing on the spliced mRNA in normal cells. We thus postulated that the mRNA re-splicing is controlled by specific repressors and we searched for repressor candidates by siRNA-based screening for mRNA re-splicing activity. We found that knock-down of EIF4A3, which is a core component of the exon junction complex (EJC), significantly promoted mRNA re-splicing. Remarkably, we could recapitulate cancer-specific mRNA re-splicing in normal cells by knock-down of any of the core EJC proteins, EIF4A3, MAGOH or RBM8A (Y14), implicating the EJC core as the repressor of mRNA re-splicing often observed in cancer cells. We propose that the EJC core is a critical mRNA quality control factor to prevent over-splicing of mature mRNA.

Structure of the human core transcription-export complex reveals a hub for multivalent interactions

The export of mRNA from nucleus to cytoplasm requires the conserved and essential transcription and export (TREX) complex (THO–UAP56/DDX39B–ALYREF). TREX selectively binds mRNA maturation marks and licenses mRNA for nuclear export by loading the export factor NXF1–NXT1. How TREX integrates these marks and achieves high selectivity for mature mRNA is poorly understood. Here, we report the cryo-electron microscopy structure of the human THO–UAP56/DDX39B complex at 3.3 Å resolution. The seven-subunit THO–UAP56/DDX39B complex multimerizes into a 28-subunit tetrameric assembly, suggesting that selective recognition of mature mRNA is facilitated by the simultaneous sensing of multiple, spatially distant mRNA regions and maturation marks. Two UAP56/DDX39B RNA helicases are juxtaposed at each end of the tetramer, which would allow one bivalent ALYREF protein to bridge adjacent helicases and regulate the TREX–mRNA interaction. Our structural and biochemical results suggest a conserved model for TREX complex function that depends on multivalent interactions between proteins and mRNA.

Nonsequential Splicing Events Alter Antisense-Mediated Exon Skipping Outcome in COL7A1

The COL7A1 gene encodes homotrimer fibrils essential for anchoring dermal and epidermal layers, and pathogenic mutations in COL7A1 can cause recessive or dominant dystrophic epidermolysis bullosa. As a monogenic disease gene, COL7A1 constitutes a potential target for antisense oligomer-mediated exon skipping, a therapy applicable to a growing number of other genetic disorders. However, certain characteristics of COL7A1: many exons, low average intron size, and repetitive and guanine-cytosine rich coding sequence, present challenges to the design of specific and effective antisense oligomers. While targeting COL7A1 exons 10 and 73 for excision from the mature mRNA, we discovered that antisense oligomers comprised of 2′-O-methyl modified bases on a phosphorothioate backbone and phosphorodiamidate morpholino oligomers produced similar, but distinctive, splicing patterns including excision of adjacent nontargeted exons and/or retention of nearby introns in some transcripts. We found that the nonsequential splicing of certain introns may alter pre-mRNA processing during antisense oligomer-mediated exon skipping and, therefore, additional studies are required to determine if the order of intron removal influences multiexon skipping and/or intron retention in processing of the COL7A1 pre-mRNA.

RNA Splicing by the Spliceosome

The spliceosome removes introns from messenger RNA precursors (pre-mRNA). Decades of biochemistry and genetics combined with recent structural studies of the spliceosome have produced a detailed view of the mechanism of splicing. In this review, we aim to make this mechanism understandable and provide several videos of the spliceosome in action to illustrate the intricate choreography of splicing. The U1 and U2 small nuclear ribonucleoproteins (snRNPs) mark an intron and recruit the U4/U6.U5 tri-snRNP. Transfer of the 5′ splice site (5′SS) from U1 to U6 snRNA triggers unwinding of U6 snRNA from U4 snRNA. U6 folds with U2 snRNA into an RNA-based active site that positions the 5′SS at two catalytic metal ions. The branch point (BP) adenosine attacks the 5′SS, producing a free 5′ exon. Removal of the BP adenosine from the active site allows the 3′SS to bind, so that the 5′ exon attacks the 3′SS to produce mature mRNA and an excised lariat intron.

The spatial distributions of pre-mRNAs suggest post-transcriptional splicing of specific introns within endogenous genes

Splicing is the molecular process by which introns are removed from pre-mRNA and exons are joined together to form the sequence of the mature mRNA. Measuring the timing of splicing relative to the transcription of nascent RNA has yielded conflicting interpretations. Biochemical fractionation suggests that RNA is spliced primarily during the process of transcription, but imaging of nascent RNA suggests that splicing happens after the process of transcription has been completed. We use single molecule RNA FISH together with expansion microscopy to measure the spatial distribution of nascent and partially spliced transcripts in mammalian cells, allowing us to infer the delay between when an intron is transcribed and when it is spliced out of a pre-mRNA. We show that 4 out of 4 genes we interrogated exhibit some post-transcriptional splicing, and that introns can be spliced in any order. We also show that completely synthesized RNA move slowly through a transcription site proximal zone while they undergo additional splicing and potentially other processing after transcription is completed. In addition, upon leaving this zone, some genes’ transcripts localize to speckles during the process of splicing but some appear to traffic freely through the nucleus without localizing to any other nuclear compartment. Taken together, our observations suggest that the regulation of the timing and localization of splicing is specific to individual introns, as opposed to the previously surmised immediate excision of introns after transcription.

Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells

The stochasticity of gene expression presents significant challenges to the modeling of genetic networks. A two-state model describing promoter switching, transcription, and messenger RNA (mRNA) decay is the standard model of stochastic mRNA dynamics in eukaryotic cells. Here, we extend this model to include mRNA maturation, cell division, gene replication, dosage compensation, and growth-dependent transcription. We derive expressions for the time-dependent distributions of nascent mRNA and mature mRNA numbers, provided two assumptions hold: 1) nascent mRNA dynamics are much faster than those of mature mRNA; and 2) gene-inactivation events occur far more frequently than gene-activation events. We confirm that thousands of eukaryotic genes satisfy these assumptions by using data from yeast, mouse, and human cells. We use the expressions to perform a sensitivity analysis of the coefficient of variation of mRNA fluctuations averaged over the cell cycle, for a large number of genes in mouse embryonic stem cells, identifying degradation and gene-activation rates as the most sensitive parameters. Furthermore, it is shown that, despite the model’s complexity, the time-dependent distributions predicted by our model are generally well approximated by the negative binomial distribution. Finally, we extend our model to include translation, protein decay, and auto-regulatory feedback, and derive expressions for the approximate time-dependent protein-number distributions, assuming slow protein decay. Our expressions enable us to study how complex biological processes contribute to the fluctuations of gene products in eukaryotic cells, as well as allowing a detailed quantitative comparison with experimental data via maximum-likelihood methods.

The Wg and Dpp morphogens regulate gene expression by modulating the frequency of transcriptional bursts

ABSTRACTMorphogen signaling contributes to the proper spatiotemporal expression of genes during development. One method of regulation of signaling-responsive genes is at the level of transcription. Single-cell quantitative studies of transcription have revealed that transcription occurs intermittently, in bursts. Although the effects of many gene regulatory mechanisms on transcriptional bursting have been studied, it remains unclear how morphogen gradients affect this dynamic property of downstream genes. Here we have adapted smFISH for use in the Drosophila wing imaginal disc in order to measure nascent and mature mRNA of genes downstream of the Wg and Dpp morphogen gradients. We compared our experimental results with predictions from stochastic models of transcription. Our results indicate that the transcription levels of these genes appear to share a common method of control via burst frequency modulation. Our data helps further elucidate the link between developmental gene regulatory mechanisms and transcriptional bursting.

Assessment of branch point prediction tools to predict physiological branch points and their alteration by variants

Background: Branch points (BPs) map within short motifs upstream of acceptor splice sites (3’ss) and are essential for splicing of pre-mature mRNA. Several BP-dedicated bioinformatics tools, including HSF, SVM-BPfinder, BPP, Branchpointer, LaBranchoR and RNABPS were developed during the last decade. Here, we evaluated their capability to detect the position of BPs, and also to predict the impact on splicing of variants occurring upstream of 3’ss. Results: We used a large set of constitutive and alternative human 3’ss collected from Ensembl (n = 264,787 3’ss) and from in-house RNAseq experiments (n = 51,986 3’ss). We also gathered an unprecedented collection of functional splicing data for 120 variants (62 unpublished) occurring in BP areas of disease-causing genes. Branchpointer showed the best performance to detect the relevant BPs upstream of constitutive and alternative 3’ss (99.48 % and 65.84 % accuracies, respectively). For variants occurring in a BP area, BPP emerged as having the best performance to predict effects on mRNA splicing, with an accuracy of 89.17 %. Conclusions: Our investigations revealed that Branchpointer was optimal to detect BPs upstream of 3’ss, and that BPP was most relevant to predict splicing alteration due to variants in the BP area. Keywords: Branch Point, Prediction, RNA, Benchmark, HSF, SVM-BPfinder, BPP, Branchpointer, LaBranchoR, RNABPS, Variants